Another branch of work has roots in primal-dual optimization methods, such as Generalized Method of Multipliers and Alternating Direction Method of Multipliers (ADMM) [1]}, [2]}, [3]} that have enjoyed much success in distributed optimization. Such methods provide a general framework which not only encompasses a wide range of objectives—such as low-rank matrix completion, nonconvex neural network training, and reinforcement learning [4]}, [5]}, [6]}—but it can also be tailored for specific application demands, such as event-triggered communication and inexact computing [7]}, [8]}, [9]}, [10]}, [11]}.
